UNet [1]}: The encoder part of the network has 10 convolutional blocks, 2 at each down-sampled size. The input is down-sampled by 4x. The decoder part of the network predicts the output by up-sampling the bottleneck and using skip connections from the encoder network by concatenating the corresponding upsampled output with the corresponding down-sampled output. The decoder part of the network also has 10 convolutional blocks, 2 after each up-sampled size and has 256 stacked channels. The total number of learnable parameters in UNet baseline is equal to 0.471 million in 2-D and 1.412 million in 3-D.
